To illustrate the proposed methods, we integrated four ovarian cancer studies. These methods for high-dimensional covariance matrix estimation with missing data are also useful for other types of data integration. For example, linking multiple data sources such as electronic data records, medicare data, registry data and patient reported outcomes could greatly increase the power of exploratory studies such as phenome-wide association studies (Denny et al. [1]}). However, missing data inevitably arises and may hinder the potential of integrative analysis. In addition to random missingness due to unavailable information on a small fraction of patients, many variables such as the genetic measurements may only exist in one or two data sources and are hence structurally missing for other data sources. Our proposed methods could potentially provide accurate recovery of the covariance matrix in the presence of missingness.
